Suppose I want to take 4 clusters of points. Each cluster can be within a given set of x-y co ordinates. Each point within a cluster is a random generated point.
These clusters will be acting as input for my K-Means clustering problem. How do I do it using Python?
This might give you some ideas:
from random import random
import math
def rand_cluster(n,c,r):
"""returns n random points in disk of radius r centered at c"""
x,y = c
points = []
for i in range(n):
theta = 2*math.pi*random()
s = r*random()
points.append((x+s*math.cos(theta), y+s*math.sin(theta)))
return points
This function can be used in various ways, for example:
def rand_clusters(k,n,r, a,b,c,d):
"""return k clusters of n points each in random disks of radius r
where the centers of the disk are chosen randomly in [a,b]x[c,d]"""
clusters = []
for _ in range(k):
x = a + (b-a)*random()
y = c + (d-c)*random()
clusters.extend(rand_cluster(n,(x,y),r))
return clusters
A typical all would look like
clusters = rand_clusters(4,50,0.3,0,1,0,1)
This generates 4 clusters of size 50 of radius 0.3
with centers chosen at random in the unit square. Points from a typical run:
If you generate data using
min + (max - min) * random()
Then they will be between min and max.
Do this for x and y, and for every cluster.
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